Justin Carpentier of Inria, École normale supérieure, Paris, France

Talk Synopsis

Over the past decades, optimization has emerged as a key enabler for many robotics applications, particularly humanoids and quadrupeds. While initial solutions largely relied on models, in today’s data-driven landscape, it is becoming increasingly common to seek data-driven extensions to classical approaches. Differentiable control architectures propose a principled way to achieve this evolution. In this keynote, I will highlight recent contributions from our group towards this objective, notably our efforts to:

(i) lay the groundwork for the next generation of differentiable simulators for robotics and

(ii) develop differentiable optimization solvers that are both fast and dependable.

Hopefully, these contributions will catalyze the design of the next generation of data-driven planning and control methods for robotics.

Biography:

Justin Carpentier is a researcher at Inria and École Normale Supérieure, heading the Willow research team since 2023. He graduated from École Normale Supérieure Paris-Saclay in 2014 and received a Ph.D. in Robotics in 2017 from the University of Toulouse. He did his Ph.D. in the Gepetto team at LAAS-CNRS in Toulouse, working on the computational foundations of legged locomotion. In 2024, he received an ERC Starting Grant focusing on laying the algorithmic and computational foundations of Artificial Motion Intelligence. 
His research interests lie at the interface of optimization, machine learning, computer vision, simulation, and control for robotics, with applications ranging from agile locomotion to dexterous manipulation. He is also the leading developer and manager of widely used open-source robotics software, among them Pinocchio, ProxSuite, HPP-FCL, and Aligator. – https://jcarpent.github.io/